Interested in this Data Scientist role at TikTok?
Apply Now →Skills & Technologies
About This Role
Data analysis
Senior Data Scientist, Global E-commerce Seller Business - Global E-Commerce Data Strategy
Location :
Seattle
Employment Type :
Regular
Job Code :
A249400
Responsibilities
Global E-commerce is a discovery-driven e-commerce business, working to become a destination for users to discover and buy good products at affordable prices. We build a shopping experience that feels tailored to each user, and a platform merchants can rely on across live and short-video commerce. The Data Strategy team supports data, strategy, and modeling needs across Europe, the Americas, and global platform initiatives. We partner with engineering, product, operations, data engineering, and data science teams to build data and strategy capabilities, develop metric frameworks, run business and product analysis, improve data infrastructure, and turn business and user data into insight that drives growth across markets. The challenge is building scalable data capabilities and trusted metric frameworks across markets that define their data differently and run on different business models. Sitting inside the Global E-commerce engineering organization, we are the connection between technical execution and business strategy. You will build the metric frameworks and data capabilities the business makes its decisions on. Global E-commerce is a content E-commerce business with international short video products as the carrier. It is committed to becoming the first choice for users to discover and purchase good products at affordable prices. Global E-commerce business team hopes to provide users with more tailored and efficient consumption experience, enabling merchants to receive stable and reliable platform services in different scenarios such as live E-commerce, short video content E-commerce, so as to make more affordable and high-quality products sell easily and a better life within reach. We are seeking a highly experienced and business-driven Senior Data Scientist to join the TikTok Shop team, focusing on the US Seller Business. In this role, you will be a key opinion leader in driving US seller growth, optimizing their lifecycle, and maximizing their success on the platform. You will operate at the intersection of data, technology, and business strategy to unlock actionable insights, build advanced models, and drive key business decisions. As a Senior Data Scientist, you will independently lead complex data projects and business directions, taking end-to-end ownership from problem identification to strategic execution. You will collaborate closely with cross-functional teams, including Product, Operations, and Engineering, to ensure our data solutions effectively address US seller pain points and accelerate business growth. Responsibilities: - Drive US Seller Growth: Lead the data strategy for the US Seller Business, identifying critical business problems, growth opportunities, and actionable insights to accelerate seller acquisition, activation, and retention on TikTok Shop. - Advanced Analytics & Modeling: Design and implement robust data models, statistical frameworks, and experimentation (A/B testing) to understand seller behaviors, optimize e-commerce funnels, and solve complex, open-ended business challenges. - End-to-End Project Ownership: Act as the owner of medium-to-large complexity projects or business directions. Translate business pain points into clear data solutions and actionable plans, ensuring high-quality, end-to-end delivery that impacts business metrics. - Cross-Functional Collaboration: Partner effectively with cross-functional stakeholders (Product Managers, Operations, Engineering) to drive consensus on key data, technical, and business decisions. Proactively manage project dependencies, mitigate risks, and ensure seamless execution across teams. - Data Infrastructure & Tooling: Construct and continuously optimize data infrastructure, core metrics, and analytical toolkits. Identify opportunities to improve team efficiency through the reuse of data capabilities and methodologies. - Mentorship & Leadership: Serve as a technical expert within the team. Guide and mentor junior data scientists, review analytical and technical designs, and foster a culture of objective, data-driven decision-making.
Qualifications
Minimum Qualifications: - Bachelor's degree in Computer Science, Statistics, Mathematics, Economics, or a related quantitative field. - 5+ years of industry experience in data science, analytics, or a related field, with a proven track record of driving business impact through data. - Deep understanding of the e-commerce ecosystem, particularly with a strong focus on US seller growth, merchant operations, or marketplace dynamics. - Exceptional analytical and modeling skills, with proficiency in SQL, Python/R, statistical analysis, and machine learning techniques. - Demonstrated ability to independently lead complex projects, break down ambiguous problems into actionable steps, and deliver end-to-end results. - Excellent cross-functional collaboration and communication skills, with the ability to navigate complex stakeholder relationships, build consensus, and present insights to both technical and non-technical audiences. Preferred Qualifications: - Hands-on experience in leveraging and building AI-driven tools (e.g., LLMs, Agents, AI workflows) to improve analytical efficiency and solve business problems. Ability to evaluate and refine AI outputs effectively. - Familiarity with or experience working on e-commerce open platforms, developer ecosystems, or API integrations for merchants. - Proven experience mentoring team members and elevating the technical and analytical standards of a data organization. - Highly self-driven, objective, and resilient, with a strong sense of ownership and a results-oriented mindset.
Job Information
【For Pay Transparency】Compensation Description (Annually)
The base salary range for this position in the selected city is $207480 - $368220 annually.
Compensation may vary outside of this range depending on a number of factors, including a candidate’s qualifications, skills, competencies and experience, and location. Base pay is one part of the Total Package that is provided to compensate and recognize employees for their work, and this role may be eligible for additional discretionary bonuses/incentives, and restricted stock units.
Benefits may vary depending on the nature of employment and the country work location. Employees have day one access to medical, dental, and vision insurance, a 401(k) savings plan with company match, paid parental leave, short-term and long-term disability coverage, life insurance, wellbeing benefits, among others. Employees also receive 10 paid holidays per year, 10 paid sick days per year and 17 days of Paid Personal Time (prorated upon hire with increasing accruals by tenure).
The Company reserves the right to modify or change these benefits programs at any time, with or without notice.
For Los Angeles County (unincorporated) Candidates:
Qualified applicants with arrest or conviction records will be considered for employment in accordance with all federal, state, and local laws including the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act. Our company believes that criminal history may have a direct, adverse and negative relationship on the following job duties, potentially resulting in the withdrawal of the conditional offer of employment:
- Interacting and occasionally having unsupervised contact with internal/external clients and/or colleagues;
- Appropriately handling and managing confidential information including proprietary and trade secret information and access to information technology systems; and
- Exercising sound judgment.
About TikTok
TikTok is the leading destination for short-form mobile video. At TikTok, our mission is to inspire creativity and bring joy. TikTok's global headquarters are in Los Angeles and Singapore, and we also have offices in New York City, London, Dublin, Paris, Berlin, Dubai, Jakarta, Seoul, and Tokyo.
Why Join Us
Inspiring creativity is at the core of TikTok's mission. Our innovative product is built to help people authentically express themselves, discover and connect – and our global, diverse teams make that possible. Together, we create value for our communities, inspire creativity and bring joy - a mission we work towards every day.
We strive to do great things with great people. We lead with curiosity, humility, and a desire to make impact in a rapidly growing tech company. Every challenge is an opportunity to learn and innovate as one team. We're resilient and embrace challenges as they come. By constantly iterating and fostering an "Always Day 1" mindset, we achieve meaningful breakthroughs for ourselves, our company, and our users. When we create and grow together, the possibilities are limitless. Join us.
Diversity & Inclusion
TikTok is committed to creating an inclusive space where employees are valued for their skills, experiences, and unique perspectives. Our platform connects people from across the globe and so does our workplace. At TikTok, our mission is to inspire creativity and bring joy. To achieve that goal, we are committed to celebrating our diverse voices and to creating an environment that reflects the many communities we reach. We are passionate about this and hope you are too.
TikTok Accommodation
TikTok is committed to providing reasonable accommodations in our recruitment processes for candidates with disabilities, pregnancy, sincerely held religious beliefs or other reasons protected by applicable laws. If you need assistance or a reasonable accommodation, please reach out to us at https://tinyurl.com/RA-request
Salary Context
This $207K-$368K range is above the 75th percentile for Data Scientist roles in our dataset (median: $161K across 138 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 9,079 AI roles we're tracking, Data Scientist positions make up 4% of the market. At TikTok, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $190,000 based on 1,503 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($287K) sits 52% above the category median. Disclosed range: $207K to $368K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
TikTok AI Hiring
TikTok has 45 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager, Research Scientist, AI Agent Developer. Positions span San Jose, CA, US, Seattle, WA, US, Singapore, Singapore. Compensation range: $208K - $456K.
Location Context
AI roles in Seattle pay a median of $226,100 across 1,045 tracked positions. That's 6% above the national median.
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (4,384) and senior (3,343) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.